In recent years, miniature spectrometers have been found useful in many applications to resolve spectrum signature of
objects or materials. In this paper, algorithms for filter-array spectrum sensor to realize miniature spectrometers are
investigated. Conventionally, the filter-array spectrum sensor can be modeled as an over-determined problem, and the
spectrum can be reconstructed by solving a set of linear equations. On the contrary, we model the spectrum
reconstruction process as an under-determined problem, and bring up the concept of template-selection by sparse
representation. L1-minimization algorithm is tested to achieve a high reconstruction resolution. Simulation results
show superior quality of spectrum reconstruction can be made possible from this under-determined approach.